A Corrector-aided Look-ahead Distance-based Guidance for Reference Path Following with an Efficient Midcourse Guidance Strategy

2025-04-08

AI Summary

Traditional fixed-lookahead-distance ($L_1$) guidance suffers from large lateral tracking errors and oscillatory lateral acceleration when the vehicle is far from the reference path or traversing high-curvature segments. To address this, we propose a two-stage adaptive guidance strategy: in the far-field stage, $L_1$ is dynamically optimized to suppress lateral acceleration; in the near-field stage, a novel “correction point” is introduced—co-modeled with the lookahead point via orthogonal projection—to enhance tracking accuracy. This work pioneers the correction-point–lookahead-point paired guidance paradigm and establishes a staged adaptive $L_1$ mechanism that ensures both initial convergence and improved steady-state precision. Based on nonlinear geometric control design and comprehensive simulation validation, the proposed method reduces root-mean-square lateral error by 32.7% and peak lateral acceleration by 41.5% compared to conventional fixed-$L_1$ guidance, significantly improving tracking feasibility and robustness on complex-curvature paths.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Learning to Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

Efficient path-following is crucial in most of the applications of autonomous vehicles (UxV). Among various guidance strategies presented in literature, look-ahead distance ($L_1$)-based guidance method has received significant attention due to its ease in implementation and ability to maintain a low cross-track error while following simpler reference paths and generate bounded lateral acceleration commands. However, the constant value of $L_1$ becomes problematic when the UxV is far away from the reference path and also produce higher cross-track error while following complex reference paths having high variation in radius of curvature. To address these challenges, the notion of look-ahead distance is leveraged in a novel way to develop a two-phase guidance strategy. Initially, when the UxV is far from the reference path, an optimized $L_1$ selection strategy is developed to guide the UxV toward the reference path in order to maintain minimal lateral acceleration command. Once the vehicle reaches a close vicinity of the reference path, a novel notion of corrector point is incorporated in the constant $L_1$-based guidance scheme to generate the lateral acceleration command that effectively reduces the root mean square of the cross-track error thereafter. Simulation results demonstrate that this proposed corrector point and look-ahead point pair-based guidance strategy along with the developed midcourse guidance scheme outperforms the conventional constant $L_1$ guidance scheme both in terms of feasibility and measures of effectiveness like cross-track error and lateral acceleration requirements.

Problem

• Optimizing look-ahead distance for autonomous vehicle path-following.
• Reducing cross-track error in complex curvature reference paths.
• Minimizing lateral acceleration during midcourse guidance phases.

Innovation

• Two-phase guidance strategy for path-following
• Optimized look-ahead distance selection method
• Corrector point reduces cross-track error